{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
      "0                0.38             0.53               2                   157   \n",
      "1                0.80             0.86               5                   262   \n",
      "2                0.11             0.88               7                   272   \n",
      "3                0.72             0.87               5                   223   \n",
      "4                0.37             0.52               2                   159   \n",
      "\n",
      "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
      "0                   3              0     1                      0  sales   \n",
      "1                   6              0     1                      0  sales   \n",
      "2                   4              0     1                      0  sales   \n",
      "3                   5              0     1                      0  sales   \n",
      "4                   3              0     1                      0  sales   \n",
      "\n",
      "   salary  \n",
      "0     low  \n",
      "1  medium  \n",
      "2  medium  \n",
      "3     low  \n",
      "4     low  \n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "hr_data = pd.read_csv('data/hr.csv', header=0)\n",
    "print (hr_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10cdcd8d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1040583c8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10505e400>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10d4e6240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10d53f9b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10d53f9e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x10d6113c8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10e80b080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10e8679b0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gkD5Z0vw87ixJyumbSro8p8+TNLG5m2hmZmZm3aCWJ55zgKlladcDe0XEG4HfAzMBJO0B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\noSS14nLwiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cdcd0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hr_data[hr_data.dtypes[(hr_data.dtypes==\"float64\")|(hr_data.dtypes==\"int64\")].index.values].hist(figsize=[11,11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1111b0940>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gKIEJAMBQAhMAgKEEJgAAQwlMAACGEpgAAAwlMAEAGEpgAgAwlMAEAGAogQkAwFACEwCA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\nJTABABhKYAIAMNR/Bd2zMvm2EPptAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1111b00f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "hr_data_scaler=scaler.fit_transform(hr_data[['satisfaction_level']])\n",
    "#hr_data_scaler = scaler.fit(hr_data_scaler)\n",
    "hr_data_scaler_df = pd.DataFrame(hr_data_scaler)\n",
    "hr_data_scaler_df.max()\n",
    "hr_data_scaler_df[hr_data_scaler_df.dtypes[(hr_data_scaler_df.dtypes==\"float64\")|(hr_data_scaler_df.dtypes==\"int64\")].index.values].hist(figsize=[11,11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1112462b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1115d5e80>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11162d518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111693c50>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AlCRJUlMGTEmSJDVlwJQkSVJTBkxJkiQ1ZcCUJElSUwZMSZIkNWXAlCRJUlMG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\n6pQDpiRJkjrlgClJkqRO/QdR+sAmXlAw9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111236a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "minmax=MinMaxScaler()\n",
    "hr_data_minmax=minmax.fit_transform(hr_data[[ 'average_montly_hours',\n",
    "                'last_evaluation', 'number_project', 'satisfaction_level']])\n",
    "hr_data_minmax_df = pd.DataFrame(hr_data_minmax)\n",
    "hr_data_minmax_df.min()\n",
    "hr_data_minmax_df.max()\n",
    "hr_data_minmax_df[hr_data_minmax_df.dtypes[(hr_data_minmax_df.dtypes==\"float64\")|(hr_data_minmax_df.dtypes==\"int64\")].index.values].hist(figsize=[11,11])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
